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相关概念视频

Aliasing01:18

Aliasing

238
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
238
Bandpass Sampling01:17

Bandpass Sampling

265
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
265
Sampling Methods: Overview01:06

Sampling Methods: Overview

535
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
535
Sampling Theorem01:15

Sampling Theorem

777
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
777
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

450
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
450
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

357
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
357

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相关实验视频

Updated: Sep 18, 2025

Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements
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基于频域转换和频道意识的对抗性样本生成方法

Yalin Gao1, Dongwei Xu1, Huiyan Zhu1

  • 1Institute of Cyberspace Security, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.

Sensors (Basel, Switzerland)
|June 27, 2025
PubMed
概括

本研究介绍了一种超分辨率排斥残余网络 (SDRNet),用于在正交频分割复杂化 (OFDM) 系统中准确估算通道. 通过增强在杂,色的通道中的特征提取,SDRNet提高了通信可靠性,并指导了对抗性攻击.

科学领域:

  • 无线通信无线通信
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 直角频率分割多重复合 (OFDM) 系统面临的挑战是由于低分辨率特征和噪声干扰,在准确的频道估计.
  • 像最小平方 (LS) 和最小平均平方误差 (MMSE) 这样的现有方法在频率选择性的色通道中扎着性能退化.

研究的目的:

  • 提出一个新的超分辨率排斥剩余网络 (SDRNet),用于在OFDM系统中增强道估计.
  • 通过开发频域对抗性攻击方法,调查准确的通道估计对通信安全的影响.
  • 为了证明SDRNet在传统道估计算法上的优势.

主要方法:

  • 通过整合超分辨率卷积神经网络 (SRCNN) 和拒绝卷积神经网络 (DnCNN) 的原则开发了SDRNet.
  • 训练有素的SDRNet使用基于试点的OFDM数据损坏了高斯噪声.
  • 提出了一个频域对抗性攻击,利用SDRNet输出,结合富里埃变换,高斯噪声,选择性掩盖和频道梯度信息.

主要成果:

  • 在平均平方错误 (MSE) 和比特错误率 (BER) 方面,SDRNet显著优于传统的LS和MMSE方法.
  • 在10dB的信号噪声比率下达到0.01以下的BER,证明了卓越的可靠性.
  • 拟议的通道意识的对抗性攻击实现了79.9%的成功率,比非通道意识的方法提高了16.3%.
关键词:
敌对攻击是对抗性的攻击.频道估计 频道估计深度学习是一种深度学习.频域转换的频率域转换无线通信系统的无线通信系统.

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结论:

  • 在具有挑战性的OFDM环境中,SDRNet为准确的通道估计提供了强大的解决方案.
  • 准确的通道估计对于提高通信可靠性和对抗性攻击的有效性至关重要.
  • 开发的对抗性攻击方法强调了精确的道状态信息的安全影响.